Numbers don't lie, but a bad chart decision makes it extremely difficult to understand what those numbers mean. Before you put together another PowerPoint presentation, make sure your pick the right type of chart to clearly communicate the information you want to share. Here's how.

Why Is the Chart Type Important?

When I was an physics student—and again when I worked in a lab—I learned that working with and collecting huge amounts of data was rewarding, but that data is only as good as how well you can communicate what it means. It's easy to throw your data up on a scatter or bar chart, slip it into a presentation, and convince yourself you'll do the explaining, but that's a terrible shortcut. When the presentation is over and the only thing left behind are your slides, no one will have a clue what your chart was trying to communicate.

The problem is that there are so many chart types, styles, and methods of presenting data that it can be confusing and difficult to pick the right one. Data fanatics like statistician and computer scientist Edward Tufte have committed their lives to helping people learn to better present information, and here are a few tips and tools inspired by their work that can help you.

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First, Understand the Message You're Trying to Present with Your Data

When you're putting together a chart, you're trying to show one of four things with the data you have: a relationship between data points, a comparison of data points, a composition of data, or a distribution of data.

A relationship tries to show a connection or correlation between two or more variables through the data presented, like the market cap of a given stock over time versus overall market trend.

A comparison tries to set one set of variables apart from another, and display how those two variables interact, like the number of visitors to five competing web sites in a single month.

A composition tries to collect different types of information that make up a whole and display them together, like the search terms that those visitors used to land on your site, or how many of them came from links, search engines, or direct traffic.

A distribution tries to lay out a collection of related or unrelated information simple to see how it correlates, if at all, and to understand if there's any interaction between the variables, like the number of bugs reported during each month of a beta.

Next, Select the Best Arrangement

Once you understand what message you're trying to send with the data you have, it's time to select the best method for displaying that information. Different chart types cater best to different methods. For example, scatter plots are best used to show distributions, while line charts (essentially, scatterplots with a defined trend) are better suited for relationships. Pie charts do well when you're trying to communicate a composition, but make for poor comparisons or distributions (although Tufte would argue that there's no good use for a pie chart.)

Finally, Format Your Chart

Once you've selected the right type of chart for your data, make sure you don't do your data a disservice by forgetting some basic design tips. Kill the grid lines unless they're absolutely necessary, or at least make them subtle so they don't distract from the information you're trying to present. Make sure your chart is centered on the data you want to present, your axes are clearly labeled, and your axes have units on them where necessary, so no one has to guess or infer what you're trying to say. Remember, your goal is that anyone can pick up your chart, whether you're there to talk to it or not, and understand what information the data is trying to communicate.

Experiment with New Presentation Methods

All of these suggestions will get you started, but there are no hard and fast rules for how data should be presented, aside from that however it's presented, it should be clear, communicative, and speak for itself. If you find that you're restrained by common chart types, then by all means branch out to more experimental techniques. There's no reason not to let your inner designer sit down with your inner statistician—together the two of you can come up with some intelligent and informative methods to present information, and you won't have to fall back on pie charts and bar graphs to do it. Photo via XKCD.

What do you think? Do you stick to specific chart types for your data, or design your own diagrams from scratch? Share your tips in the comments below.